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An explainable covariate compartmental model for predicting the spatio-temporal patterns of dengue in Sri Lanka

Fig 2

Structure of hybrid compartment model (A) and lag configuration selection (B).

(A) At each time step, t, covariates (household income, population rate of age over 60, mean temperature, precipitation, mean NDVI) and newly infected cases, and the hidden state h_t from the previous time step are encoded by the LSTM model. The output, , from LSTM model is then passed into a linear layer with a sigmoid activation function and amplified by a scaling coefficient to approximate the time-varying force of infection λ_t. The compartment model with S, E, I, R representing susceptible, exposed, infected and recovered compartments, respectively, are driven by λ_t. (B) We perform lag configuration selection on the validation set by shifting climate factors with different lag time steps (no lag, 1-3 week, 4-6 week, 7-9 week, 10-12 week) and get the best performance lag, worst performance lag configuration. Then we repeat the same selection for all the climate covariates. At last, we combine the best performance lag configurations of all the climate covariates to select the best lag model.

doi: https://doi.org/10.1371/journal.pcbi.1013540.g002